3m Innovative Properties Company

Attenti Electronic Monitoring Ltd

Nicholas Andrew Asendorf

Jennifer Frances Schumacher

Jonathan Dale Gandrud

Robert W Shannon

Saber Taghvaeeyan

David Solomon Segal

Arash Sangari

Guruprasad Somasundaram

James W Howard

Himanshu Nayar

Deepti Pachauri

Gautam Singh

Muhammad Jamal Afridi

Nitsan Ben-Gal Nguyen

Mojtaba Kadkhodaie Elyaderani

James Bevan Snyder

Robert D Lorentz

James B Snyder

Golshan Golnari

Ronald David Jesme

Steven T Awiszus

Kiran S Kanukurthy

Eric C Lobner

G Marco Bommarito

Glenn E Casner

Justin M Johnson

Shannon D Scott

Andrew H Tilstra

Claire R Donoghue

Tyler William Olson

James William Howard

Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm along with location and optional time information. The extracted features/information of a test sample are evaluated by the trained classification algorithm to identify the test sample. The results of the evaluation are used to track and locate counterfeits or authentic products.

Image based counterfeit detection

Systems and methods for detecting/identifying novel material samples are provided. A test sample image is processed with a trained transformation function to obtain a transformed matrix. A measure of similarity of the test image based on the transformed matrix is compared to a threshold to determine whether the test sample is novel to a batch of material samples that are provided to train the transformation function.

Image based novelty detection of material samples

Example techniques of this disclosure are directed determining one or more values that represent a monitoring attribute for one or more body-worn tracking devices (BWTDs). In some instances, the techniques include determining a compliance metric that represents a level of compliance for at least one offender. That is, the compliance metric may be a quantitative value that represents whether a user wearing BWTD is complying with established rules or desired behaviors.

Compliance metrics for offender monitoring devices

Systems and methods for authenticating material samples are provided. Digital images of the samples are processed to extract computer-vision features, which are used to train a classification algorithm. The computer-vision features of a test sample are evaluated by the trained classification algorithm to identify the test sample.

A body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) device, at least one motion sensor, at least one processor, and at least one memory device. The at least one memory device includes instructions that, when executed by the at least one processor, cause the at least one processor to determine, based on data generated by the at least one motion sensor, a net distance between the last known location of the BWTD and a current location of the BWTD. The instructions further cause the at least one processor to determine, based on the net distance, whether the BWTD is within a bounded area that includes the last known location; and responsive to determining that BWTD is not within the bounded area, output an indication that the BWTD is not within the bounded area.

Geographic boundary compliance detection using body-worn offender monitoring electronic devices

Aspects of the present disclosure related to a computer system and method for analyzing whether a package has been opened. The system includes a memory and one or more processors communicatively coupled to the memory and can be configured to receive an image corresponding to a wrapped package. The one or more processors can also be configured to generate a set of feature values based on a visual representation of the wrapped package in the image. The one or more processors can also be configured to apply the set of feature values to at least one model that is trained based at least in part on a set of images that include the wrapped package having the status, to determine the status of the wrapped package.

Vision system for status detection of wrapped packages

A system for tracking a location of a body-worn tracking device (BWTD) includes a global navigation satellite system (GNSS) component, one or more sensors, one or more processors, and a memory device. The memory device includes instructions that, when executed by the one or more processors, cause the one or more processors to determine whether a current location of the BWTD can be determined using the GNSS component. The instructions also cause the one or more processors to determine, based in part on the activity of the wearer of the BWTD and based on a determination that the current location of the BWTD cannot be determined using the GNSS component, whether to perform an action. The instructions also cause the one or more processors to perform the action in response to a determination to perform the action.

Activity-based rules for compliance detection using body-worn offender monitoring electronic devices

In an example, a system comprises at least one body-worn tracking device (BWTD) configured to transmit location data that indicates a location of the BWTD and a computing system configured to communicate with the at least one BWTD. The computing system is configured to receive the location data from the at least one BWTD, determine, based on a set of weighted filters associated with at least one monitoring attribute, at least one gradient value corresponding to the location data, where the at least one gradient value represents a level of the at least one monitoring attribute, and perform at least one operation based on the at least one gradient value.

Characterizing monitoring attributes for offender monitoring

In one example, a system includes one or more personal protective equipment (PPE) devices each configured to be worn by a worker, the PPE devices each including one or more sensors that generate activity data indicative of activities of workers operating within one or more work environments. The system also includes a computing device, the computing device configured to: identify, based at least on the activity data, a plurality of clusters of one or more entities, wherein each entity of the entities is associated with one or more of the workers; and output an indication of a difference between performance by a target entity with respect to safety events and performance by the cluster that includes the target entity with respect to safety events.

Personal protective equipment and safety management system for comparative safety event assessment

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Saint Paul, MN, United States of America

Nicholas Andrew Asendorf